Feature detection and matching play important roles in many fields of computer vision, such as image understanding, feature recognition, 3D-reconstruction, video analysis, etc. Extracting features is usually the first...Feature detection and matching play important roles in many fields of computer vision, such as image understanding, feature recognition, 3D-reconstruction, video analysis, etc. Extracting features is usually the first step for feature detection or matching, and the gradient feature is one of the most used selections. In this paper, a new image feature-absence importance （AI） feature, which can directly characterize the local structure information, is proposed. Greatly different from the most existing features, the proposed absence importance feature is mainly based on the consideration that the absence of the important pixel will have a great effect on the local structure. Two absence importance features, mean absence importance （MAI） and standard deviation absence importance （SDAI）, are defined and used subsequently to construct new algorithms for feature detection and matching. Experiments demonstrate that the proposed absence importance features can be used as an important complement of the gradient feature and applied successfully to the fields of feature detection and matching.展开更多
基金supported by National Natural Science Foundation of China(Nos.61201395,61272394,61472119 and 61472373)the program for Science & Technology Innovation Talents in Universities of Henan Province(No.13HASTIT039)the Program for Young Backbone Teachers in Universities of Henan Province(Nos.2012GGJS-057 and 2013GGJS-052)
文摘Feature detection and matching play important roles in many fields of computer vision, such as image understanding, feature recognition, 3D-reconstruction, video analysis, etc. Extracting features is usually the first step for feature detection or matching, and the gradient feature is one of the most used selections. In this paper, a new image feature-absence importance （AI） feature, which can directly characterize the local structure information, is proposed. Greatly different from the most existing features, the proposed absence importance feature is mainly based on the consideration that the absence of the important pixel will have a great effect on the local structure. Two absence importance features, mean absence importance （MAI） and standard deviation absence importance （SDAI）, are defined and used subsequently to construct new algorithms for feature detection and matching. Experiments demonstrate that the proposed absence importance features can be used as an important complement of the gradient feature and applied successfully to the fields of feature detection and matching.
